{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T21:24:04Z","timestamp":1782854644837,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T00:00:00Z","timestamp":1580947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41431177"],"award-info":[{"award-number":["41431177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871300"],"award-info":[{"award-number":["41871300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["Project No. XDA23100503"],"award-info":[{"award-number":["Project No. XDA23100503"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil\u2013environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the location-skipping scheme does not predict these locations and, thus, completely ignores the potentially useful information provided by valid covariate values. The void-filling scheme may introduce errors when applying an interpolation algorithm to removing NoData environmental covariate values. In this study, we propose a new scheme called FilterNA that conducts DSM for each individual location with NoData value of a covariate by using the valid values of other covariates at the location. We design a new method (SoLIM-FilterNA) combining the FilterNA scheme with a DSM method, Soil Land Inference Model (SoLIM). Experiments to predict soil organic matter content in the topsoil layer in Anhui Province, China, under different test scenarios of NoData for environmental covariates were conducted to compare SoLIM-FilterNA with the SoLIM combined with the void-filling scheme, the original SoLIM with the location-skipping scheme, and random forest. The experimental results based on the independent evaluation samples show that, in general, SoLIM-FilterNA can produce the lowest errors with a more complete spatial coverage of the DSM result. Meanwhile, SoLIM-FilterNA can reasonably predict uncertainty by considering the uncertainty introduced by applying the FilterNA scheme.<\/jats:p>","DOI":"10.3390\/ijgi9020102","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T03:13:27Z","timestamp":1581045207000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Nai-Qing","family":"Fan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5725-0460","authenticated-orcid":false,"given":"A-Xing","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing, Jiangsu 210023, China"},{"name":"Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5910-9807","authenticated-orcid":false,"given":"Cheng-Zhi","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3098-9633","authenticated-orcid":false,"given":"Peng","family":"Liang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"ref_1","unstructured":"Goodchild, M.F., Parks, B.O., and Steyaert, L.T. (1993). Environmental Modeling with GIS, Oxford University Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"W08418","DOI":"10.1029\/2006WR005313","article-title":"Plant response to the soil environment: An analytical model integrating yield, water, soil type, and salinity","volume":"43","author":"Shani","year":"2007","journal-title":"Water Resour. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.2136\/sssaj2011.0025","article-title":"Digital soil mapping and modeling at continental scales: Finding solutions for global issues","volume":"75","author":"Grunwald","year":"2011","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1002\/ldr.2656","article-title":"S-world: A global soil map for environmental modelling","volume":"28","author":"Stoorvogel","year":"2017","journal-title":"Land Degrad. Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0016-7061(03)00223-4","article-title":"On digital soil mapping","volume":"117","author":"McBratney","year":"2003","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.2136\/sssaj2001.6551463x","article-title":"Soil mapping using GIS, expert knowledge, and fuzzy logic","volume":"65","author":"Zhu","year":"2001","journal-title":"Soil Sci Soc. Am. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.geoderma.2015.07.017","article-title":"Digital soil mapping: A brief history and some lessons","volume":"264","author":"Minasny","year":"2016","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"523","DOI":"10.2136\/sssaj1997.03615995006100020022x","article-title":"Derivation of soil properties using a soil land inference model (SoLIM)","volume":"61","author":"Zhu","year":"1997","journal-title":"Soil Sci Soc. Am. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ishioka, T. (2012, January 3\u20135). Imputation of missing values for semi-supervised data using the proximity in random forests. Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services, Bali, Indonesia.","DOI":"10.1145\/2428736.2428793"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geoderma.2013.07.020","article-title":"Digital mapping of soil salinity in Ardakan region, central Iran","volume":"213","author":"Minasny","year":"2014","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Little, R.J., and Rubin, D.B. (2019). Statistical Analysis with Missing Data, John Wiley & Sons. [2nd ed.].","DOI":"10.1002\/9781119482260"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5194\/essd-5-3-2013","article-title":"The Northern Circumpolar Soil Carbon Database: Spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions","volume":"5","author":"Hugelius","year":"2013","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_13","first-page":"97","article-title":"Reduction of errors in digital terrain parameters used in soil-landscape modelling","volume":"5","author":"Hengl","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.geoderma.2008.05.008","article-title":"Soil organic carbon concentrations and stocks on Barro Colorado Island - Digital soil mapping using Random Forests analysis","volume":"146","author":"Grimm","year":"2008","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hengl, T., Heuvelink, G.B., Kempen, B., Leenaars, J.G., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., Mendes de Jesus, J., and Tamene, L. (2015). Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0125814"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.geoderma.2015.06.023","article-title":"Mapping of soil properties and land degradation risk in Africa using MODIS reflectance","volume":"263","author":"Winowiecki","year":"2016","journal-title":"Geoderma"},{"key":"ref_17","unstructured":"McBratney, A.B., and Walvoort, D.J.J. (2001, January 19\u201321). Generalised Linear Model Kriging: A generic framework for kriging with secondary data. Proceedings of the Pedometrics 2001 4th Conference of the Working Group on Pedometric of the IUSS, Ghent, Belgium."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G., and Walsh, M.G. (2014). SoilGrids1km\u2014global soil information based on automated mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105992"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.geodrs.2014.11.003","article-title":"Evaluating digital soil mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France)","volume":"4","author":"Vaysse","year":"2015","journal-title":"Geoderma Reg."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotic, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids 250 m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.spasta.2015.06.002","article-title":"Sampling for regression-based digital soil mapping: Closing the gap between statistical desires and operational applicability","volume":"13","year":"2015","journal-title":"Spat. Stat."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1111\/ejss.12244","article-title":"Predictive soil mapping with limited sample data","volume":"66","author":"Zhu","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.geoderma.2011.06.006","article-title":"Mapping soil organic matter in small low-relief catchments using fuzzy slope position information","volume":"171\u2013172","author":"Qin","year":"2012","journal-title":"Geoderma"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.geoderma.2010.05.001","article-title":"Prediction of soil properties using fuzzy membership values","volume":"158","author":"Zhu","year":"2010","journal-title":"Geoderma"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1080\/19475683.2018.1534890","article-title":"Spatial prediction based on Third Law of Geography","volume":"24","author":"Zhu","year":"2018","journal-title":"Ann. GIS"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/S1002-0160(17)60322-9","article-title":"Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach","volume":"27","author":"Yang","year":"2017","journal-title":"Pedosphere"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.geoderma.2017.03.014","article-title":"Identification of representative samples from existing samples for digital soil mapping","volume":"311","author":"An","year":"2018","journal-title":"Geoderma"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1080\/07038992.1994.10874583","article-title":"A knowledge-based approach to data integration for soil mapping","volume":"20","author":"Zhu","year":"1994","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/136588199241382","article-title":"A personal construct-based knowledge acquisition process for natural resource mapping","volume":"13","author":"Zhu","year":"1999","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/B978-0-12-405942-9.00001-3","article-title":"Digital Mapping of Soil Carbon","volume":"118","author":"Minasny","year":"2013","journal-title":"Adv. Agron."},{"key":"ref_31","first-page":"1195","article-title":"Measuring uncertainty in class assignment for natural resource maps under fuzzy logic","volume":"63","author":"Zhu","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_32","first-page":"737","article-title":"Simple digital terrain analysis software (SimDTA 1.0) and its application in fuzzy classification of slope positions","volume":"11","author":"Qin","year":"2009","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_34","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pantanowitz, A., and Marwala, T. (2008). Evaluating the Impact of Missing Data Imputation through the use of the Random Forest Algorithm. arXiv.","DOI":"10.1007\/978-3-642-03156-4_6"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_38","first-page":"1","article-title":"Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests","volume":"17","author":"Mentch","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.geoderma.2016.12.017","article-title":"Using quantile regression forest to estimate uncertainty of digital soil mapping products","volume":"291","author":"Vaysse","year":"2017","journal-title":"Geoderma"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/2\/102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:55:28Z","timestamp":1760172928000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/2\/102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,6]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["ijgi9020102"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9020102","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,6]]}}}